10 uses cases - Artificial Intelligence and Machine Learning in Sales and Marketing - by ai.business

Machine Learning use in
Sales & Marketing
All rights reserved. AI.Business Owner: http://ai.business

USE CASE – Affinio:
Audience insights analysis

Affinio – Audience insights analysis:
EFFECTS OF USAGE
• Allows clients to understand customers as people, based on their
interests and passions, by leveraging the social graph;
• Helps clients to develop data-driven content that will resonate with
the targeted customers;
• Identifies the best channels to distribute and promote the client’s
content (like websites, social networks, brand partners, influencers,
and celebrities).
Source: http://www.affinio.com/approach

USE CASE – Percolata: Helping retailers
predict in-store customer traffic
Utilizing physical big data to
dramatically improve how brick
and mortar businesses work

Percolata: Helping retailers predict in-store
customer traffic: EFFECTS OF USAGE
• The free traffic counters contributes to tracking the customer count for retail
stores and then project traffic going forward for the website.
• The plug-and-play sensors and predictive analytics give the most accurate
occupancy rates. Major benefits include saved manager time, increased revenue,
and the improvement of customer loyalty.
• The process takes two weeks to get started and less than a month to see tangible
return of investment.
• The platform helps the increase of sales and cut out of costs with the auto-
schedule feature that eliminates the problem of both over- and understaffing.
Source: http://www.percolata.com/

USE CASE – Prelert: Behavioral
analytics for payment security
Prelert automates behavioral analytics allowing its customers
to discover real-time insights while minimizing the upfront
investment

Prelert - Behavioral analytics for payment
security: EFFECTS OF USAGE
• Machine Learning (ML) are at play to flag any malpractice in very high volume high
frequency data transactions / communications.
• ML powered systems can now detect a possible insider trading in a stock market,
also ML can flag a rogue customer transaction as a fraudulent transaction in high
volume business doing market place websites.
• Among the benefits are:
– Analyze Operational Metrics
– Discover Root Cause
– Track Business KPIs
Source: http://info.prelert.com/products/anomaly-detective-engine

USE CASE – Azure ML: Sales
Forecasting
Powerful cloud based
analytics

Azure ML – Sales Forecasting:
EFFECTS OF USAGE
• The platform comes loaded with many different samples that include models to predict credit
risk, customer churn, flight delays, and many others which will help you predict different
scenarios.
• Azure ML allows you to include multiple prediction scenarios in the same experiment and
components to easily compare the results.
• The platform provides many tools for data analysis, but the most comfortable choice is to do
most of the work in excel and just upload .csv file into the workspace.
• Azure ML offers the possibility to test different sets of columns and different algorithms so
that you can compare the results and pick the best performing model.
Source: http://www.skylinetechnologies.com/Insights/Skyline-Blog/February-2015/Sales-
Forecasting-using-Azure-Machine-Learning

USE CASE – Predicting Performance of
Fundraising Campaigns
Two major components:
1)Linguistic features, which include
a)Uni, bi and tri-grams from the
project description and the section
on risks, for predictive phrases.
b)Psycholinguistic features (LIWC -
Linguistic Inquiry and Word Count)
for categories (cognitive,
inhibition)
c) Sentiment scores from
comments on project page
2. Non-text metadata, as provided
in the image:

Predicting Performance of Fundraising
Campaigns: EFFECTS OF USAGE
• The platform tackles the potential success of a fundraising campaign (for example,
using kickstarter), based only on the initial description of the project - something
that the project creator has full control over.
• It focuses on the language of the project description - in particular, phrases which
are predictive of success- and their psycholinguistic qualities - in addition to other
metadata.
• Training and dev sets are used for experimenting with different estimators and
model selection for the campaigns.
Source: http://cs229.stanford.edu/proj2015/239_poster.pdf

USE CASE – SmartReply: Computer
Virtual Assistant
Google launched
Smart Reply, a deep
learning network that
writes short email
responses for you.

SmartReply - Computer Virtual
Assistant: EFFECTS OF USAGE
• The Smart Reply System is built on a pair of recurrent neural networks, one that is
used to encode the incoming email and one to predict possible responses.
• The system can automatically determine if an email is answerable with a short
reply, and composes a few suitable responses to that.
• The use can edit or send with just a tap.
• The Smart Reply feature was especially designed to the same rigorous user privacy
standards any user would want: no humans reading your email.
• The email chat box has come to understand the semantic similarity between two
responses and now it's possible to suggest responses that are different not only in
wording but in their underlying meaning as well.
Source: http://googleresearch.blogspot.ro/2015/11/computer-respond-to-this-email.html

USE CASE – RichRelevance:
omnichannel personalization
The RichRelevance
personalization
products work across
mobile, web, in store,
and other channels to
create relevant
experiences that span
the continuum of the
customer lifecycle.

RichRelevance: omnichannel
personalization: EFFECTS OF USAGE
• The platform helps you leverage the omnichannel data to create a
360-degree view of your customer’s shopping behaviors and
preferences, so that you can engage them with highly relevant
experiences on your website.
• The RichRelevance platform allows you to to personalize the entier
shopper journey from engagement to product discovery to
checkout on mobille devices.
• Marketers can now focus on introducing customized, curated
content and messaging through personalization.
Source: http://www.richrelevance.com/omnichannel/

USE CASE – Yelp: Making user-
generated content valuable
Yelp hosts tens of millions of
photos uploaded by Yelpers from
all around the world. And this
wide variety of photos provides
a rich window into local
businesses.

Yelp - Making user-generated content
valuable: EFFECTS OF USAGE
• Yelp is mainly used for checking out the atmosphere for a special event or
navigating to a venue or a new place to eat. The business detail pages show a
set of “cover photos” which are recommended by our photo scoring engine
based on user feedback and certain photo attributes.
• This develops a photo understanding system which allows us to create
semantic data about individual photographs.
• The data generated by the system has been powering our recent launch of
tabbed photo browsing as well as our first attempts at content-based photo
diversification.
Source: http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-
to-classify-business-photos-at-yelp.html

USE CASE – Barilliance: Personalized
product recommendations
Targeting customers with
personalized recommendations
across multiple pages and
multiple channels

Barilliance - Personalized product
recommendations: EFFECTS OF USAGE
• Product recommendations are setup to take domain specific attributes that are important for
your site (i.e. size, color, brands) matching their design.
• The platform makes it possible for emails with product recommendations to be sent to users
based on their most recent activity on your site.
• You can decide on which pages the content will be visible and do it yourself in a few minutes
without IT involvement.
• The system is configured to capture data instead of asking the IT department to send it.
• Once the system starts to collect data, there is a learning period in which data is analyzed (2-
4 weeks depending on your traffic and catalog size).
Source: http://www.barilliance.com/product-recommendations-engine/

USE CASE – Lumidatum: Improving
Customer Service
Smart Data Discovery for
the Customer Experience

Lumidatum: Improving Customer
Service: EFFECTS OF USAGE
• The platform uses algorithms to discover deep insights about your customers and then
turning the data into a competitive advantage by identifying what offer, when to send it and
to whom.
• The Lumidatum machine learning system let's you find new revenue streams fueling the
growth of your business.
• You can use data science to optimize merchandising, make product recommendations and
identify your best customers.
• The platform helps you provide a dynamic and personalized experience for your customers
boosting loyalty and engagement.
• It enables data preparation and cleansing through model building to utilizing and launching
predictions in your own apps and platforms.
Source: https://www.lumidatum.com/

10 uses cases - Artificial Intelligence and Machine Learning in Sales and Marketing - by ai.business

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10 uses cases - Artificial Intelligence and Machine Learning in Sales and Marketing - by ai.business